The analysis of the NSW wheat variety database. II. Variance component estimation

Cullis, Brian R; Thomson, F.; Fisher, John Andrew; Gilmour, A.; Thompson, R.

doi:10.1007/bf00222948

Cited by 53 publications

(40 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This result is consistent with other studies that have used the CPD to evaluate METs network efficiency (Cullis et al, 1996;Ceretta & van Eeuwijk, 2008;Arief et al, 2015). The factor that showed the strongest impact on the CPD indicator in our study was the number of locations, followed by the number of replications in each experiment (Fig.…”

Section: Met Network Efficiencysupporting

confidence: 93%

Genotype by environment interaction in sunflower (Helianthus annus L.) to optimize trial network efficiency

González-Barrios¹,

Castro²,

Pérez³

et al. 2018

Span. j. agric. res.

View full text Add to dashboard Cite

Modeling genotype by environment interaction (GEI) is one of the most challenging aspects of plant breeding programs. The use of efficient trial networks is an effective way to evaluate GEI to define selection strategies. Furthermore, the experimental design and the number of locations, replications, and years are crucial aspects of multi-environment trial (MET) network optimization. The objective of this study was to evaluate the efficiency and performance of a MET network of sunflower (Helianthus annuus L.). Specifically, we evaluated GEI in the network by delineating mega-environments, estimating genotypic stability and identifying relevant environmental covariates. Additionally, we optimized the network by comparing experimental design efficiencies. We used the National Evaluation Network of Sunflower Cultivars of Uruguay (NENSU) in a period of 20 years. MET plot yield and flowering time information was used to evaluate GEI. Additionally, meteorological information was studied for each sunflower physiological stage. An optimal network under these conditions should have three replications, two years of evaluation and at least three locations. The use of incomplete randomized block experimental design showed reasonable performance. Three mega-environments were defined, explained mainly by different management of sowing dates. Late sowings dates had the worst performance in grain yield and oil production, associated with higher temperatures before anthesis and fewer days allocated to grain filling. The optimization of MET networks through the analysis of the experimental design efficiency, the presence of GEI, and appropriate management strategies have a positive impact on the expression of yield potential and selection of superior cultivars.Additional keywords: genotype by environment interaction; multi-environment trials; sunflower; network efficiency; yield stability. Abbreviations used: CPD (critical percentage difference); GEI (genotype by environment interaction); GGE (genotype plus genotype by environment); LE (La Estanzuela); MET (multi-environment trial); NENSU (National evaluation network of sunflower cultivars of Uruguay); PLS (partial least squares); RCBD (randomized complete block design); YG (Young).

show abstract

Section: Met Network Efficiencysupporting

confidence: 93%

Genotype by environment interaction in sunflower (Helianthus annus L.) to optimize trial network efficiency

González-Barrios¹,

Castro²,

Pérez³

et al. 2018

Span. j. agric. res.

View full text Add to dashboard Cite

show abstract

“…Hereafter, a two-stage, mixed model approach was utilized [23] using ASReml [24]. Each experiment was analyzed separately with the best spatial models being determined after first fitting the experimental design and then modelling the residual variation with autoregressive row and column terms.…”

Section: Discussionmentioning

confidence: 99%

The Potential of Lr19 and Bdv2 Translocations to Improve Yield and Disease Resistance in the High Rainfall Wheat Zones of Australia

et al. 2015

View full text Add to dashboard Cite

Chromosomal translocations in wheat derived from alien species are a valuable source of genetic diversity that have provided increases in resistance to various diseases and improved tolerance to abiotic stresses in wheat. These alien genomic segments can also affect multiple traits, with a concomitant ability to alter yield potential in either a positive or negative fashion. The aim of this work was to characterize the effects on yield of two types of translocations, namely T4-derived translocations from Thinopyrum ponticum, carrying the leaf rust resistance gene Lr19, and the TC14 translocation from Th. intermedium, carrying the barley yellow dwarf virus resistance gene Bdv2, in Australian adapted genetic backgrounds and under Australian conditions. A large range of germplasm was developed by crossing donor sources of the translocations into 24 Australian adapted varieties producing 340 genotypes. Yield trials were conducted in 14 environments to identify effects on yield and yield components. The T4 translocations had a positive effect on yield in one high yielding environment, but negatively affected yield in low-yielding environments. The TC14 translocation was generally benign, however, it was associated with a negative impact on yield and reduced height in two genetic backgrounds. OPEN ACCESSAgronomy 2015, 5 56The translocation was also associated with a delayed maturity in several backgrounds. The T4 translocations results were consistent with previously published data, whilst this is the first time that such an investigation has been undertaken on the TC14 translocation. Our data suggests a limited role for each of these translocations in Australia. The T4 translocations may be useful in high yielding environments, such as under irrigation in NSW and in the more productive high rainfall regions of south-eastern Australia. Traits associated with the TC14 translocation, such as BYDV resistance and delayed maturity, would make this translocation useful in BYDV-prone areas that experience a less pronounced terminal drought (e.g., south-eastern Australia).

show abstract

“…Spatial model Gleeson 1998, Cullis et al 1998) was fitted in the first stage for each trait at each site. The BLUE (Best linear unbiased estimators) and weights (Cullis et al 1996) were calculated and used in the second stage analysis. Protein level is confounded with yield level due to the negative phenotypic correlation between yield and protein level (believed to be largely a dilution effect).…”

Section: Discussionmentioning

confidence: 99%

Genetic gain in yield and protein over two cycles of a wheat recurrent selection program

et al. 2010

View full text Add to dashboard Cite

Increasing grain yield and protein are important wheat breeding goals. However, the simultaneous improvement of both traits is complicated by a strong negative correlation and genotype by environment interaction (G×E). A recurrent selection program (RSP) reported here was initiated to produce germplasm, which combined both high yield-potential and increased protein content for the northern wheat-growing region of Australia. Diallel crossing amongst 10 cultivars (6 high yield and 4 high quality cultivars), followed by random mating amongst the F1s were used to produce the founding segregating population. Two years of multienvironment trials were conducted in the GRDC Northern Grain Region (NR) for each of the foundation (RSA0) and first (RSA1) cycles of recurrent selection. Data from the two cycles were analysed to evaluate the genetic gain achieved over the two cycles of selection, based on five potential selection strategies. Selection based on yield or protein or yield-adjusted protein only increased selected trait but reduced the other traits. However, application of selection based on yield and protein or yield and yield-adjusted protein simultaneously increased both yield (4.7%, 6.0% for RSA0 and 4.0%, 4.2% for RSA1 respectively) and protein (2.1%, 1.3% for RSA0 and 1.7%, 1.6% for RSA1 respectively).

show abstract

The analysis of the NSW wheat variety database. II. Variance component estimation

Cited by 53 publications

References 21 publications

Genotype by environment interaction in sunflower (Helianthus annus L.) to optimize trial network efficiency

Genotype by environment interaction in sunflower (Helianthus annus L.) to optimize trial network efficiency

The Potential of Lr19 and Bdv2 Translocations to Improve Yield and Disease Resistance in the High Rainfall Wheat Zones of Australia

Genetic gain in yield and protein over two cycles of a wheat recurrent selection program

Contact Info

Product

Resources

About